Read our paper in Journal of Virology
R and R packages
We ran R scripts with R v3.6.2. We used the following packages installable from CRAN:
dplyr(v0.85)rvest(v0.3.5)rworldmap(v1.3-6)ggplot2(v3.3.0)
Python and python packages:
We ran python scripts with Python 3.7, installed using Anaconda. See docstrings of *.py files for further information on reproducing results. Packages were installed with pip3 and include:
matplotlibmmh3
SARS-CoV2 and other coronavirus protein sequences
We manually downloaded the full polyprotein 1ab (ORF1ab), spike (S) protein, membrane (M) protein, envelope (E) protein, and nucleocapsid (N) protein sequence FASTA files for the following 34 coronavirus species from the National Center of Biotechnology Information (NCBI) Reference Sequence Database. Note that for SARS-CoV-2 and SARS-CoV we downloaded FASTA files for all additional annotated protein sequences comprising their full viral proteomes:
| Name | ORF1ab | Spike | Envelope | Membrane | Nucleocapsid |
|---|---|---|---|---|---|
| SARS-CoV-2* | YP_009724389.1 | YP_009724390.1 | YP_009724392.1 | YP_009724393.1 | YP_009724397.2 |
| SARS-CoV* | NP_828849.2 | NP_828851.1 | NP_828854.1 | NP_828855.1 | NP_828858.1 |
| OC43 | YP_009555238.1 | YP_009555241.1 | YP_009555243.1 | YP_009555244.1 | YP_009555245.1 |
| Bovine-CoV | NP_150073.2 | NP_150077.1 | NP_150081.1 | NP_150082.1 | NP_150083.1 |
| HKU24 | YP_009113022.1 | YP_009113025.1 | YP_009113028.1 | YP_009113029.1 | YP_009113031.1 |
| HKU1 | YP_173236.1 | YP_173238.1 | YP_173240.1 | YP_173241.1 | YP_173242.1 |
| MHV | AAU06353.1 | AAU06356.1 | AAU06359.1 | AAU06360.1 | NP_045302.1 |
| Rat-CoV | YP_003029844.1 | YP_003029848.1 | YP_003029850.1 | YP_003029851.1 | YP_003029852.1 |
| Bat-BCoV | YP_009072438.1 | YP_009072440.1 | YP_009072442.1 | YP_009072443.1 | YP_009072446.1 |
| Hedgehog-CoV | YP_009513008.1 | YP_009513010.1 | YP_009513016.1 | YP_009513017.1 | YP_009513018.1 |
| MERS-CoV | YP_009047202.1 | YP_009047204.1 | YP_009047209.1 | YP_009047210.1 | YP_009047211.1 |
| HKU4 | YP_001039952.1 | YP_001039953.1 | YP_001039958.1 | YP_001039959.1 | YP_001039960.1 |
| HKU5 | YP_001039961.1 | YP_001039962.1 | YP_001039967.1 | YP_001039968.1 | YP_001039969.1 |
| GCCDC1 | YP_009273004.1 | YP_009273005.1 | YP_009273007.1 | YP_009273008.1 | YP_009273009.1 |
| HKU9 | YP_001039970.1 | YP_001039971.1 | YP_001039973.1 | YP_001039974.1 | YP_001039975.1 |
| HKU14 | YP_005454239.1 | YP_005454245.1 | YP_005454247.1 | YP_005454248.1 | YP_005454249.1 |
| CDPHE15 | YP_008439200.1 | YP_008439202.1 | YP_008439204.1 | YP_008439205.1 | YP_008439206.1 |
| HKU10 | YP_006908641.2 | YP_006908642.1 | YP_006908644.1 | YP_006908645.1 | YP_006908646.1 |
| BtRf-AlphaCoV | YP_009199789.1 | YP_009199790.1 | YP_009199792.1 | YP_009199793.1 | YP_009199794.1 |
| 229E | ARU07599.1 | ARU07601.1 | ARU07603.1 | ARU07604.1 | ARU07605.1 |
| LuchengRn-CoV | YP_009336483.1 | YP_009336484.1 | YP_009336485.1 | YP_009336486.1 | YP_009336487.1 |
| Ferret-CoV | YP_009256195.1 | YP_009256197.1 | YP_009256199.1 | YP_009256200.1 | YP_009256201.1 |
| Mink-CoV | YP_009019180.1 | YP_009019182.1 | YP_009019184.1 | YP_009019185.1 | YP_009019186.1 |
| Bat-CoV-1A | YP_001718603.1 | YP_001718605.1 | YP_001718607.1 | YP_001718608.1 | YP_001718609.1 |
| HKU8 | YP_001718610.1 | YP_001718612.1 | YP_001718614.1 | YP_001718615.1 | YP_001718616.1 |
| BtMr-AlphaCoV | YP_009199608.1 | YP_009199609.1 | YP_009199611.1 | YP_009199612.1 | YP_009199613.1 |
| BtNv-AlphaCoV | YP_009201729.1 | YP_009201730.1 | YP_009201732.1 | YP_009201733.1 | YP_009201734.1 |
| Porcine-EDV | NP_598309.2 | NP_598310.1 | NP_598312.1 | NP_598313.1 | NP_598314.1 |
| BtCoV512 | YP_001351683.1 | YP_001351684.1 | YP_001351686.1 | YP_001351687.1 | YP_001351688.1 |
| HKU2 | YP_001552234.1 | YP_001552236.1 | YP_001552238.1 | YP_001552239.1 | YP_001552240.1 |
| NL63 | YP_003766.2 | YP_003767.1 | YP_003769.1 | YP_003770.1 | YP_003771.1 |
| NL63-related | YP_009328933.1 | YP_009328935.1 | YP_009328937.1 | YP_009328938.1 | YP_009328939.1 |
| FCoV | YP_004070193.2 | YP_004070194.1 | YP_004070197.1 | YP_004070198.1 | YP_004070199.1 |
| TGE | NP_058422.1 | NP_058424.1 | NP_058426.1 | NP_058427.2 | NP_058428.1 |
HLA allele frequency data
We designed and executed a custom R script (HLA_frequencies.R) to scrape all population and human leukocyte antigen (HLA) allele frequency from the Allele Frequency Net Database.
Protein sequence alignments
For each protein class (i.e. ORF1ab, S, M, E, N), all 34 coronavirus sequences were aligned using the web-based Clustal Omega multisequence aligner tool employing default parameters: sequence type [Protein], output alignment format [clustal_num], dealign [false], mBed-like clustering guide-tree [true], mBed-like clustering iteration [true], number of combined iterations [0], maximum guide tree iterations [-1], and maximum HMM iterations [-1].
Binding affinity
Using the above FASTA sequences, we used netchop v3.0 "C-term" model with a cleavage threshold of 0.1 to filter peptides that were not predicted to undergo canonical MHC class I antigen processing via proteasomal cleavage.
To assess binding affinity, we kmerized the protein FASTA into peptides of length 8-12.
sed 's/>.*/|/g' protein_sequence.fasta > protein_sequence_with_pipe.fasta
perl -pe 's/\s+//g' protein_sequence_with_pipe.fasta > protein_no_space.fasta
From the netchop v3.0 output, we further processed the file to get rid of NA lines and added row numbers as a column.
sed '/NA/d' netchop_out.txt > netchop_out_parsed.txt
awk -F'\t' 'NR>1{$0=$0"\t"NR-1} 1' netchop_out_parsed.txt > netchop_numbered.txt
Using these 2 files, we ran a custom python script (faster_kmer.py) to generate a list of peptides.
python3 faster_kmer.py > output.pep
sed '/|/d' output.pep > output_filt.pep
Binding affinity of these peptides were calculated using netMHCpan v4.0 (73) with the ‘-BA’ option to include binding affinity predictions and the ‘-l’ option to specify peptides of lengths 8-12.